The following books contain basic introductions to statistical language modeling:
\begin{itemize}
\item {\em Spoken Dialogues with Computers}, by Renato DeMori, chapter 7.
\item {\em Speech  and Language Processing},  by Dan  Jurafsky and  Jim Martin, chapter 6.
\item {\em Foundations   of  Statistical   Natural  Language   Processing},  by C. Manning and H. Schuetze.
\item {\em Statistical Methods for Speech Recognition}, by Frederick Jelinek.
\item {\em Spoken Language Processing}, by Huang, Acero and Hon.
\end{itemize}

\noindent
The following papers describe the IRST LM toolkit:
\begin{itemize}

\item Efficient data structures to handle huge language models:
\begin{quote}
Marcello Federico and Mauro Cettolo, {\em Efficient Handling of N-gram Language Models for Statistical Machine Translation}, In Proc. of the Second Workshop on Statistical Machine Translation, pp. 88--95, ACL, Prague, Czech Republic, 2007.
\end{quote}

\item Language Model quantization:
\begin{quote}
Marcello Federico and Nicola Bertoldi, {\em How Many Bits Are Needed To Store Probabilities for Phrase-Based Translation?}, In Proc. of the Workshop on Statistical Machine Translation. pp. 94-101, NAACL, New York City, NY, 2006. 
\end{quote}


\item Language Model adaptation with mixtures:
\begin{quote}
Marcello Federico and Nicola Bertoldi, {\em Broadcast news LM adaptation over time}, Computer Speech and Language. 18(4): pp. 417-435, October, 2004.
\end{quote}
\item Language Model adaptation with MDI:
\begin{quote}
Marcello Federico, {\em Efficient LM Adaptation through MDI Estimation}. In Proc. of Eurospeech, Budapest, Hungary, 1999.
\end{quote}
\end{itemize}

